Photochemistry and chemometrics—An overview

Vis-NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai-Tibet Plateau

Soil nutrients play vital roles in vegetation growth and are a key indicator of land degradation. Accurate, rapid, and non-destructive measurement of the soil nutrient content is important for ecological conservation, degradation monitoring, and precision farming. Currently, visible and near-infrared (Vis-NIR) spectroscopy allows for rapid and non-destructive monitoring of soil nutrients. However, the performance of Vis-NIR inversion models is extremely dependent on the number of samples. Limited samples may lead to low prediction accuracy of the models. Therefore, modeling and prediction based on a small sample size remain a challenge. This study proposes a method for the simultaneous augmentation of soil spectral and nutrient data (total nitrogen (TN), soil organic matter (SOM), total potassium oxide (TK₂O), and total phosphorus pentoxide (TP₂O₅)) using a generative adversarial network (GAN). The sample augmentation range and the level of accuracy improvement were also analyzed. First, 42 soil samples were collected from the pika disturbance area on the QTP. The collected soils were measured in the laboratory for Vis-NIR and TN, SOM, TK₂O, and TP₂O₅ data. A GAN was then used to augment the soil spectral and nutrient data simultaneously. Finally, the effect of adding different numbers of generative samples to the training set on the predictive performance of a convolutional neural network (CNN) was analyzed and compared with another data augmentation method (extended multiplicative signal augmentation, EMSA). The results showed that a GAN can generate data very similar to real data and with better diversity. A total of 15, 30, 60, 120, and 240 generative samples (GAN and EMSA) were randomly selected from 300 generative samples to be included in the real data to train the CNN model. The model performance first improved and then deteriorated, and the GAN was more effective than EMSA. Further shortening the interval for adding GAN data revealed that the optimal ranges were 30-40, 50-60, 30-35, and 25-35 for TK₂O, TN, TP₂O₅, and SOM, respectively, and the validation set accuracy was maximized in these ranges. Therefore, the above method can compensate to some extent for insufficient samples in the hyperspectral prediction of soil nutrients, and can quickly and accurately estimate the content of soil TK₂O, TN, TP₂O₅, and SOM.

Near-Infrared data classification at phone terminal based on the combination of PCA and CS-RBFSVC algorithms

Near-infrared (NIR) spectroscopy is a non-destructive, efficient and convenient detection technology, with the emergence of portable NIR spectrometers, NIR mobile applications (APPs) come into being. The popularity of intelligent mobile phones provides an impetus to the research and development of NIR APPs, however, the primary functions such as operating the NIR spectrometers and collecting data cannot satisfy NIR users in the field of data processing. Herein, we propose an APP processing NIR data locally at the mobile terminal, by the comprehensive utilization of Principal Component Analysis (PCA) and Cuckoo Search algorithm optimized Support Vector Classifier with radial basis function (RBFSVC) kernel (CS-RBFSVC). 738 NIR samples of four drugs (Cydiodine Buccal Tablets, Sulfasalazine Enteric-coated Tablets, Dexamethasone Acetate Tablets, Vecuronium Bromide for Injection) were used as the validation objects to train and test the data classification model. Firstly, the original data were subjected to dimensional reduction through PCA for the purpose of compressing calculation amount. Secondly, the CS-RBFSVC model was utilized to classify the types of drugs and their manufacturers, moreover, the improved accuracy and efficiency by introducing Cuckoo Search (CS) algorithm into RBFSVC were proven in comparison with the conventional grid optimized RBFSVC (Grid-RBFSVC) and Linear Support Vector Classifier (Linear-SVC). Last but not least, an APP based on the proposed PCA and CS-RBFSVC model is developed and demonstrated to be able to classify the type of drugs with an accuracy of 100%, the accuracies of classifying the drugs' manufacturers were 100%, 100%, 98.3% and 90.7%, respectively. Conclusively, the proposed PCA and CS-RBFSVC based model can provide a low-consumption, high accuracy and quick strategy for NIR data classification and overcome the limitations of internal storage and operating speed at phone terminals, in conjunction with the portable NIR spectrometer, it is believed to push forward NIR technology into the instant detection and on-site inspection.

Fusion of Ultraviolet-Visible and Infrared Transient Absorption Spectroscopy Data to Model Ultrafast Photoisomerization

Ultrafast photoisomerization reactions generally start at a higher excited state with excess of internal vibrational energy and occur via conical intersections. This leads to ultrafast dynamics which are difficult to investigate with a single transient absorption spectroscopy technique, be it in the ultraviolet-visible (UV-vis) or infrared (IR) domain. On one hand, the information available in the UV-vis domain is limited as only slight spectral changes are observed for different isomers. On the other hand, the interpretation of vibrational spectra is strongly hindered by intramolecular relaxation and vibrational cooling. These limitations can be circumvented by fusing UV-vis and IR transient absorption spectroscopy data in a multiset multivariate curve resolution analysis. We apply this approach to describe the spectrodynamics of the ultrafast cis-trans photoisomerization around the C-N double bond observed for aromatic Schiff bases. Twisted intermediate states could be elucidated, and isomerization was shown to occur through a continuous complete rotation. More broadly, data fusion can be used to rationalize a vast range of ultrafast photoisomerization processes of interest in photochemistry.

New insight into monitoring degradation products during the TiO2-photocatalysis process by multivariate molecular spectroscopy

This study focuses on the feasibility of a spectroscopic multivariate method for monitoring the concentration of phenol and its main degradation products during heterogeneous photocatalysis. Phenolic compounds were chosen as model to evaluate the degradation process due to their toxicity and persistence in the environment and also their well-known degradation pathway. The predictive capability of the multivariate method developed by partial least squares regression (PLSR) over the spectral range of 200-350 nm was satisfactory, allowing mean predicted errors below 5.0 % in the simultaneous determination of the target compounds using six latent variables and smoothing spectra. Suitable results were reported for the simultaneous determination of hydroquinone, resorcinol, pyrocatechol, and p-benzoquinone in accordance to the chromatographic method. Characteristics such as simplicity, low cost, and fast data acquisition are remarkable in this procedure, which makes it appropriate for this type of analytical control.

Characterization of a highly efficient N-doped TiO2 photocatalyst prepared via factorial design

The preparation of a highly efficient N-doped TiO₂ photocatalyst was optimized by factorial design and the resulting powder was fully characterized.

A method for determining supernatant-free spectra generated from fluorescently stained cells in suspension

Here we present a fluorometric method for direct determination of supernatant-free fluorescence spectra generated from fluorescently stained cells in suspension. The key element in the new technique is the design of an adapter to a standard cuvette holder that makes it possible to measure front-face fluorescence spectra from thin layers of cells spun down to the bottom of a spectrofluorometric cuvette. We have demonstrated the applicability of this approach and its analytical potential using the suspensions of yeast cells stained with the potentiometric dye of 3,3'-dipropylthiadicarbocyanine, diS-C3(3), and with the specific cell-wall marker calcofluor.

A DFT/TD-DFT study of thiazolidinedione derivative in dimethylformamide: cooperative roles of hydrogen bondings, electronic and vibrational spectra

The time-dependent density functional theory (TDDFT) method has been applied to investigate the thiazolidinedione (TZD) derivative A and its hydrogen-bonded complexes with dimethylformamide (DMF) (A-DMF and A-2DMF). The calculation results showed that the excited-state hydrogen bondings of O-H⋯O=C and N-H⋯O=C are strengthened and weakened in the hydrogen-bonded trimer A-2DMF, and their cooperation effect caused a blue shift in the electronic spectrum of A-2DMF. This modulation mechanism of the hydrogen-bond strengthening and weakening and its role in influencing the spectroscopy property of the TZD derivative A in DMF have been analyzed and showed in details.

TIME-DEPENDENT DENSITY FUNCTIONAL THEORY STUDY ON THE ELECTRONIC EXCITED-STATE HYDROGEN BONDING DYNAMICS OF METHYL ACETATE IN AQUEOUS SOLUTION

The time-dependent density functional theory (TDDFT) method has been carried out to investigate the hydrogen-bonding dynamics of methyl acetate ( CH 3 CO 2 CH 3) in hydrogen-donating water solvent. The ground-state geometry optimizations, electronic transition energies and corresponding oscillation strengths of the low-lying electronically-excited states for the isolated CH 3 CO 2 CH 3 and H2O monomers, the hydrogen-bonded CH3CO2CH3-(H2O)1, 2 complexes have been calculated using DFT and TDDFT methods respectively. One intermolecular hydrogen bond C=O⋯H–O is formed between CH3CO2CH3 and one water molecule in CH3CO2CH3-H2O dimer. Meanwhile, in CH3CO2CH3-(H2O)2 trimer, two intermolecular hydrogen bonds C=O⋯H–O are formed between CH3CO2CH3 and two water molecules. By theoretically monitoring the excitation energy changes among the CH3CO2CH3 monomer, the CH3CO2CH3-H2O dimer, and the CH3CO2CH3-(H2O)2 trimer, we have demonstrated interestingly that in some electronically-excited states, the intermolecular hydrogen bonds are strengthened inducing electronic spectral redshifts, while in others weakened with electronic spectral blueshifts. The phenomenon that hydrogen bonds are strengthened in some electronic states while weakened in others can arouse further probe into CH3CO2CH3-(H2O)1, 2 complexes.

Fluorescence emission spectra of calcofluor stained yeast cell suspensions: heuristic assessment of basis spectra for their linear unmixing

Fluorescence emission spectra of yeast cell suspensions stained with calcofluor have recently been identified as promising markers of variations in the quality of yeast cell wall. It is shown in this paper how the raw fluorescence spectra of calcofluor can be transformed to reliable spectral signatures of cell wall quality, which are independent of actual dye-to-cell concentrations of examined cell suspensions. Moreover, the presented approach makes it possible to assess basis fluorescence spectra that allows for the spectral unmixing of raw fluorescence spectra in terms of respective fluorescence contributions of calcofluor solvated in the suspension medium and bound to yeast cell walls.